{"id":"https://openalex.org/W4229008799","doi":"https://doi.org/10.1145/3477314.3507007","title":"Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes","display_name":"Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4229008799","doi":"https://doi.org/10.1145/3477314.3507007"},"language":"en","primary_location":{"id":"doi:10.1145/3477314.3507007","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477314.3507007","pdf_url":null,"source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039937464","display_name":"Christian C. Haudenschild","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Christian Haudenschild","raw_affiliation_strings":["University of Minnesota School of Medicine"],"affiliations":[{"raw_affiliation_string":"University of Minnesota School of Medicine","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075327685","display_name":"Louis Vaickus","orcid":"https://orcid.org/0000-0002-8989-9539"},"institutions":[{"id":"https://openalex.org/I1289422878","display_name":"Dartmouth\u2013Hitchcock Medical Center","ror":"https://ror.org/00d1dhh09","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I107672454","https://openalex.org/I1289422878","https://openalex.org/I4390039337"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Louis Vaickus","raw_affiliation_strings":["Dartmouth Hitchcock Medical Center"],"affiliations":[{"raw_affiliation_string":"Dartmouth Hitchcock Medical Center","institution_ids":["https://openalex.org/I1289422878"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053202754","display_name":"Joshua Levy","orcid":"https://orcid.org/0000-0001-8050-1291"},"institutions":[{"id":"https://openalex.org/I1289422878","display_name":"Dartmouth\u2013Hitchcock Medical Center","ror":"https://ror.org/00d1dhh09","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I107672454","https://openalex.org/I1289422878","https://openalex.org/I4390039337"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joshua Levy","raw_affiliation_strings":["Dartmouth Hitchcock Medical Center"],"affiliations":[{"raw_affiliation_string":"Dartmouth Hitchcock Medical Center","institution_ids":["https://openalex.org/I1289422878"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5039937464"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":0.4155,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55591467,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"627","last_page":"635"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7471321225166321},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6340867280960083},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.633274257183075},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5556052327156067},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5348675847053528},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5039758086204529},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4885888993740082},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.4767104983329773},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.47446972131729126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47230982780456543},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4625370502471924},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.440211683511734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4391518235206604},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.2245904505252838},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.21296831965446472}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7471321225166321},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6340867280960083},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.633274257183075},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5556052327156067},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5348675847053528},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5039758086204529},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4885888993740082},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.4767104983329773},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.47446972131729126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47230982780456543},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4625370502471924},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.440211683511734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4391518235206604},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2245904505252838},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.21296831965446472},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477314.3507007","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477314.3507007","pdf_url":null,"source":{"id":"https://openalex.org/S4363608665","display_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2005921028","https://openalex.org/W2061326496","https://openalex.org/W2092189965","https://openalex.org/W2120751691","https://openalex.org/W2153579005","https://openalex.org/W2165748167","https://openalex.org/W2168981535","https://openalex.org/W2187089797","https://openalex.org/W2250189634","https://openalex.org/W2259469853","https://openalex.org/W2284851926","https://openalex.org/W2404901863","https://openalex.org/W2517259736","https://openalex.org/W2610332124","https://openalex.org/W2618851150","https://openalex.org/W2625625371","https://openalex.org/W2784499877","https://openalex.org/W2787483926","https://openalex.org/W2803274241","https://openalex.org/W2912269676","https://openalex.org/W2943347371","https://openalex.org/W2963543751","https://openalex.org/W3012501605","https://openalex.org/W3086590218","https://openalex.org/W3098949126","https://openalex.org/W3104523752","https://openalex.org/W3115055441","https://openalex.org/W3164573547","https://openalex.org/W3168714845"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W4255224757","https://openalex.org/W1981780420"],"abstract_inverted_index":{"Vast":[0],"quantities":[1,55],"of":[2,56,75,100,128],"electronic":[3],"patient":[4,57,102],"medical":[5],"data":[6,16,58],"are":[7,49],"currently":[8],"being":[9],"collated":[10],"and":[11,41,68,89,110,115],"processed":[12],"in":[13,119,149],"large":[14],"federated":[15,98],"repositories.":[17],"For":[18],"instance,":[19],"TriNetX,":[20],"Inc.,":[21],"a":[22,84,97,129],"global":[23],"health":[24],"research":[25,43],"network,":[26],"has":[27],"access":[28],"to":[29,51,64,94,121],"more":[30],"than":[31],"300":[32],"million":[33],"patients,":[34],"sourced":[35],"from":[36,59],"healthcare":[37],"organizations,":[38],"biopharmaceutical":[39],"companies,":[40],"contract":[42],"organizations.":[44],"As":[45],"such,":[46],"pipelines":[47],"that":[48],"able":[50],"algorithmically":[52],"extract":[53],"huge":[54],"multiple":[60],"modalities":[61],"present":[62,83],"opportunities":[63],"leverage":[65],"machine":[66,88],"learning":[67,70,91],"deep":[69,90],"approaches":[71],"with":[72,96,139,144],"the":[73,126,136],"possibility":[74],"generating":[76],"actionable":[77],"insight.":[78],"In":[79],"this":[80],"work,":[81],"we":[82],"modular,":[85],"semi-automated":[86],"end-to-end":[87],"pipeline":[92,106],"designed":[93],"interface":[95],"network":[99],"structured":[101],"data.":[103],"This":[104],"proof-of-concept":[105],"is":[107],"disease-agnostic,":[108],"scalable,":[109],"requires":[111],"little":[112],"domain":[113],"expertise":[114],"manual":[116],"feature":[117],"engineering":[118],"order":[120],"quickly":[122],"produce":[123],"results":[124],"for":[125],"case":[127],"user-defined":[130],"binary":[131],"outcome":[132],"event.":[133],"We":[134],"demonstrate":[135],"pipeline's":[137],"efficacy":[138],"three":[140],"different":[141],"disease":[142],"workflows,":[143],"high":[145],"discriminatory":[146],"power":[147],"achieved":[148],"all":[150],"cases.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}